Double sliding window variance detection-based time-of-arrival estimation in ultra-wideband ranging systems

Ultra-wideband (UWB) ranging via time-of-arrival (TOA) estimation method has gained a lot of research interests because it can take full advantage of UWB capabilities. Energy detection (ED) based TOA estimation technique is widely used in the area due to its low cost, low complexity and ease of implementation. However, many factors affect the ranging performance of the ED-based methods, especially, non-line-of-sight (NLOS) condition and the integration interval. In this context, a new TOA estimation method is developed in this paper. Firstly, the received signal is denoised using a five-level wavelet decomposition, next, a double sliding window algorithm is applied to detect the change in the variance information of the received signal, the first path (FP) TOA is then calculated according to the first variance sharp increase. The simulation results using the CM1 and CM2 IEEE 802.15.4a channel models, prove that our proposed approach works effectively compared with the conventional ED-based methods

WUB-IP : a high-precision UWB positioning scheme for indoor multi-user applications

High-precision positioning scheme, an important part of the indoor navigation system, can be implemented using an ultra-wide band (UWB) based ranging system. Recently, solutions for precise positioning in dense multi-path and non-line-of-sight (NLOS) conditions have attracted a lot of attention in literature. On the other hand, it is expected that Waveform Division Multiple Access (WDMA) technology for multi-user UWB positioning application will be indispensable in the near future. In this regard, a WDMA-UWB based positioning scheme is investigated in this paper, for enhancing the performance of positioning accuracy in multi-user applications. In accordance with practical requirements of indoor positioning, we propose a new indoor positioning scheme, termed as WUB-IP. This scheme adopts WDMA for multiple access, and utilizes an entropy-based approach for the Time of Arrival (TOA) estimation. Moreover, a transfer learning approach is used for ranging error mitigation in NLOS conditions, in order to improve the positioning accuracy in NLOS conditions. System-level simulations demonstrate that the proposed scheme enhances the performance of indoor positioning for multi-user applications